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Parametric and linear parameter varying modeling and optimization of uncertain crane systems

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Abstract

This research investigates uncertain crane motor system unstructured, structured, and linear parameter varying uncertainty modeling, control, and optimization frameworks. The proposed approaches effectively address the uncertain or time-varying plant components that typically exhibit significant variations under normal physical operational conditions with external influence, implying a complex stabilization and performance synthesis problem with a need for sophisticated quantitative frameworks, suitable for real-time implementations, as compared to traditional proportional-integral-derivative controller implementations. The crane system motor inductor component uncertainty is modeled analytically for the proposed three frameworks by using the uncertain state-space approach and the corresponding Multi-Input Multi-Output Linear Fractional Transformation modeling is used to formulate robust optimization problems for superior tracking performances under operational disturbances. The uncertain crane controller synthesis numerical results clearly indicate the effectiveness of the proposed modeling and optimization frameworks on desired tower crane stability and performance levels.

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  1. Texas A&M University-Kingsville, 700 University Blvd., MSC 192, ENGC 322, Kingsville, TX, 78363, USA

    Robert Ngabesong & Muhittin Yilmaz

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  1. Robert Ngabesong
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  2. Muhittin Yilmaz
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Correspondence to Muhittin Yilmaz.

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Ngabesong, R., Yilmaz, M. Parametric and linear parameter varying modeling and optimization of uncertain crane systems. Int. J. Dynam. Control 7, 430–438 (2019). https://doi.org/10.1007/s40435-018-0466-3

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  • DOI: https://doi.org/10.1007/s40435-018-0466-3

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